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Found 13 papers, 3 papers with code
A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with Autoencoder
The autoencoder is designed to process the LayerCAM heat maps from the local model updates, improving their distinctiveness and thereby increasing the accuracy in spotting anomalous maps and malicious local models.
Leverage Variational Graph Representation For Model Poisoning on Federated Learning
This paper puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL).
No More Ambiguity in 360° Room Layout via Bi-Layout Estimation
A unique property of our Bi-Layout model is its ability to inherently detect ambiguous regions by comparing the two predictions.
No More Ambiguity in 360deg Room Layout via Bi-Layout Estimation
Specifically on the MatterportLayout dataset it improves 3DIoU from 81. 70% to 82. 57% across the full test set and notably from 54. 80% to 59. 97% in subsets with significant ambiguity.
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach
The attacker then adversarially regenerates the graph structural correlations while maximizing the FL training loss, and subsequently generates malicious local models using the adversarial graph structure and the training data features of the benign ones.
Handling The Non-Smooth Challenge in Tensor SVD: A Multi-Objective Tensor Recovery Framework
We develop a new optimization algorithm named the Alternating Proximal Multiplier Method (APMM) to iteratively solve the proposed tensor completion model.
A Novel Tensor Factorization-Based Method with Robustness to Inaccurate Rank Estimation
This study aims to solve the over-reliance on the rank estimation strategy in the standard tensor factorization-based tensor recovery and the problem of a large computational cost in the standard t-SVD-based tensor recovery.
Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation
In this paper, we propose a novel UDA method for MDE, referred to as Learning Feature Decomposition for Adaptation (LFDA), which learns to decompose the feature space into content and style components.
DcnnGrasp: Towards Accurate Grasp Pattern Recognition with Adaptive Regularizer Learning
The task of grasp pattern recognition aims to derive the applicable grasp types of an object according to the visual information.
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT
Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT).
Cross-Domain Visual Recognition via Domain Adaptive Dictionary Learning
These intermediate domains form a smooth path and bridge the gap between the source and target domains.
Submodular Attribute Selection for Action Recognition in Video
In real-world action recognition problems, low-level features cannot adequately characterize the rich spatial-temporal structures in action videos.
Tag Taxonomy Aware Dictionary Learning for Region Tagging
Tags of image regions are often arranged in a hierarchical taxonomy based on their semantic meanings.
